Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance.

ABSTRACT:

Purpose

This work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space.

Methods

Clinical SPECT-MPI images of 345 patients acquired on a dedicated cardiac SPECT camera in list-mode format were retrospectively employed to predict standard-dose from low-dose images at half-, quarter-, and one-eighth-dose levels. To simulate realistic low-dose projections, 50%, 25%, and 12.5% of the events were randomly selected from the list-mode data through applying binomial subsampling. A generative adversarial network was implemented t

SUBMITTER: Aghakhan Olia N 

PROVIDER: S-EPMC8940834 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature